
We have used the QuickDraw Dataset created in [1]} for training and experimentation. QuickDraw consists of sketches belonging to 345 different categories. Each category consists of 75000 sketches for training, 2500 for validation and 2500 for testing. As of today, QuickDraw is the only dataset with a large number of sketches in vector format for training and testing. We trained VASkeGAN and SkeGAN on the categories of sketches such as cat, firetruck, mosquito and yoga poses, as done in [1]}. Since there is huge variety of sketches in QuickDraw, we chose these categories because they represent the sketches of humans, animals, insects and non-living things, and capture the diversity of the dataset. We have trained a separate model for each of the categories for both VASkeGAN and SkeGAN architectures, as done for Sketch-rnn in [1]}. VASkeGAN was trained for 200000 iterations on the aforesaid sketch categories. The total number of training rounds for cat, mosquito, yoga and firetruck sketches are 4, 3, 6 and 4 respectively. The results of SkeGAN and VASkeGAN along their implications are discussed subsequently. We have quantitatively assessed the visual appeal of the sketches by performing a Human Turing Test with a group of 45 human subjects, to rate the sketches on a scale of 1 – 5 on the categories such as clarity, drawing skill and naturalness. We have also introduced a metric, Ske-score, to objectively assess sketch generation in vector format.
